Big data-driven automatic generation of ship route planning in complex maritime environments
- PDF / 2,495,143 Bytes
- 8 Pages / 595 x 842 pts (A4) Page_size
- 28 Downloads / 204 Views
Big data-driven automatic generation of ship route planning in complex maritime environments Peng Han1, Xiaoxia Yang2* 1 Administrative Center for China’s Agenda 21, Beijing 100038, China 2 College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Received 19 December 2019; accepted 21 January 2020 © Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract
With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded, leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system (AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques; assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper. Key words: ship route planning, AIS, big data, trajectory data mining, electronic chart Citation: Han Peng, Yang Xiaoxia. 2020. Big data-driven automatic generation of ship route planning in complex maritime environments. Acta Oceanologica Sinica, 39(8): 113–120, doi: 10.1007/s13131-020-1638-5
1 Introduction With the inevitable rapid development of economic globalization, especially the implementation of China’s national strategy Maritime Silk Road, international trade for China is increasing at an unprecedented rate. As a result, an increasing number of ships are actively involved in maritime transport within the coastal areas of China, to meet the urgent demand for cross-continent trades. Due to the large volume of traffic flow and relatively congested sea lanes, there is a high pro
Data Loading...
